Steve Jobs said it most profoundly: "love what you do".

This is the opposite of "do what you love".

I wish I understood where he learned this.

It's very profound (and true).

Aren't the alternatives you mentioned - icerberg and duckdb - both storage solutions while spark is a way to express distributed compute? I'm a bit out of touch with this space, is there a newer way to express distributed compute?

Does it say 1982 anywhere except the pricing table and the submission title here? Is it possible that the brochure is actually newer?

Their marketing points are really understood and relatable to people who know what a pain operating Cassandra can be. The two biggest pain points IMO are read-repair and compaction - both of which must be run periodically and consume a huge amount of resources. Read Repair is especially a pain because (1) it must be run periodically or you risk losing data (2) it takes forever to complete in some deploys (ex - you must run read-repair in a certain (user-tunable) timeframe, the default of which is every 10 days - I have tables that take 7 days to complete a read-repair, meaning I have repairs pretty much running 24/7) and (3) there are no/few operational tools to manage read repair. The low-tech way is to write a cron job on every node - and even then there is no way to measure progress or detect if a job failed/completed without grepping logs - it's so bad that Spotify wrote a open source tool to manage it.

The solution has been to just buy more nodes (if you don't want long repairs, store less than 1TB of data per node) and faster disks. Read Repair maintenance is probably the only thing I hate about Cassandra - and seeing benchmarks that Scylla does these operations on the order of minutes rather than hours is attractive enough for most people (I don't think most deploys are even coming close to the benchmarked txn/s in real-world workloads, for both databases). Both compaction and repair tend to be CPU intensive (both work by essentially reading a ton of data), so I'd imagine the move to C++ and the core-per-thread design is more efficient.

In short, the operational efficiency is far more attractive even if you aren't pushing a trillion writes/sec.

I've been thinking about testing Scylla for a while, but unfortunately they don't support the features we support, and while our Cassandra deployment is a rather comparatively large cost, there are enough things on my plate right now where trading my current set of evils for other unknown ones isn't very attractive.

See this post by Discord App - https://blog.discordapp.com/how-discord-stores-billions-of-m... - where they are mentioning moving to Scylla from Cassandra for similar reasons. Performance is fine, but repair efficiency is more of the driving factor.

I'd also add that Cassandra advertises itself as a relatively high performance database for distributed workloads. If something like a faster Cassandra doesn't entice you, chances are you'd be better served by something like Postgres anyways.